# Computations
import pandas as pd
import numpy as np

# sklearn
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
from sklearn.metrics import f1_score, precision_score, recall_score, roc_curve, auc
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, cross_val_score, KFold
from sklearn.feature_selection import RFE
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer

import re

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## seaborn
import seaborn as sns
sns.set_style('whitegrid')
sns.set_context('paper', rc={'font.size':12,'axes.titlesize':14,'axes.labelsize':12})

## matplotlib
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
import matplotlib.gridspec as gridspec
import matplotlib.colors
from pylab import rcParams
from matplotlib.font_manager import FontProperties
plt.rcParams['figure.figsize'] = 14, 8
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px
# %config InlineBackend.figure_format = 'retina' 

import warnings
warnings.filterwarnings("ignore")

Daily Weather Data Analysis and Classification using Feature Importance

In this article, we analyze a weather dataset from Kaggle.com.

Table of Contents

• The Dataset
• Preprocessing
• Problem Description
• Modeling and Classification
  • Decision Tree Classifier
  • Random Forest Classifier
• Final Thoughts

Daily Weather Datase

Data description from Kaggle:

• Sensor measurements from the weather station were captured at one-minute intervals. These measurements were then processed to generate values to describe daily weather. Since this dataset was created to classify
• low-humidity days vs. non-low-humidity days (that is, days with normal or high humidity), the variables included are weather measurements in the morning, with one measurement, nAMely relatively humidity, in the afternoon. The idea is to use the morning weather values to predict whether the day will be low-humidity or not based on the afternoon measurement of relative humidity.
• Each row, or sAMple, consists of the following variables:
  • Number: unique number for each row
  • air_pressure_9am: air pressure averaged over a period from 8:55 AM to 9:04 AM (Unit: hectopascals)
  • air_temp_9am: air temperature averaged over a period from 8:55 AM to 9:04 AM (Unit: degrees Fahrenheit)
• air_wind_direction_9am: wind direction averaged over a period from 8:55AM to 9:04AM (Unit: degrees,
  • with 0 means coming from the North, and increasing clockwise)
• air_wind_speed_9am: wind speed averaged over a period from 8:55AM to 9:04AM (Unit: miles per hour) max_wind_direction_9am: wind gust direction averaged over a period from 8:55AM to 9:10AM (Unit:
  • degrees, with 0 being North and increasing clockwise)
• max_wind_speed_9am: wind gust speed averaged over a period from 8:55 AM to 9:04 AM (Unit: miles per hour)
• rain_accumulation_9am: the AMount of rain accumulated in the 24 hours before 9 AM (Unit: millimeters)
• rain_duration_9am: the AMount of time rain was recorded in the 24 hours before 9 AM (Unit: seconds)
• relative_humidity_9am: relative humidity averaged over a period from 8:55 AM to 9:04 AM (Unit: percent)
• relative_humidity_3pm: relative humidity averaged over a period from 2:55 PM to 3:04 PM (Unit: percent )

Loading the Dataset

Data = pd.read_csv('weatherdata/daily_weather.csv')
Data.drop(columns = ['number'], inplace = True)
Data.head().style.hide_index().set_precision(2)

air_pressure_9am	air_temp_9am	avg_wind_direction_9am	avg_wind_speed_9am	max_wind_direction_9am	max_wind_speed_9am	rain_accumulation_9am	rain_duration_9am	relative_humidity_9am	relative_humidity_3pm
918.06	74.82	271.10	2.08	295.40	2.86	0.00	0.00	42.42	36.16
917.35	71.40	101.94	2.44	140.47	3.53	0.00	0.00	24.33	19.43
923.04	60.64	51.00	17.07	63.70	22.10	0.00	20.00	8.90	14.46
920.50	70.14	198.83	4.34	211.20	5.19	0.00	0.00	12.19	12.74
921.16	44.29	277.80	1.86	136.50	2.86	8.90	14730.00	92.41	76.74

Features

Columns	Description
Air Pressure	Air pressure StartFragment in hectopascal (100 pascals) at 9 AM
Air Temperature	Air temperature in degrees Fahrenheit at 9 AM
Avg Wind Direction	Average wind direction over the minute before the timestamp in degrees (0 starts from the north) at 9 AM
Avg Wind Speed	Average wind speed over the minute before the timestamp in meter per seconds (m/s) at 9 AM
Max Wind Direction	Highest wind direction in the minute before the timestamp in degrees (0 starts from the north) at 9 AM
Max Wind Speed	Highest wind speed in the minute before the timestamp in meter per seconds (m/s) at 9 AM
Min Wind Speed	Smallest wind speed in the minute before the timestamp in meter per seconds (m/s) at 9 AM
Rain Accumulation	Accumulated rain in millimeters (mm) at 9 AM
Rain Duration	Length of time rain in seconds (s) at 9 AM
Relative Humidity (Morning)	Relative humidity in percentage in at 9 AM
Relative Humidity (Afternoon)	Relative humidity in percentage at 3 PM

For convenience, we would like to modify the feature names.

Data.columns = [x.replace('ty_9am','ty_(Morning)').replace('3pm', '(Afternoon)').replace('_9am', '').replace('_',
                                                      ' ').title().replace('Temp','Temperature') for x in Data.columns.tolist()]
Data.head(5).style.hide_index().set_precision(2)

Air Pressure	Air Temperature	Avg Wind Direction	Avg Wind Speed	Max Wind Direction	Max Wind Speed	Rain Accumulation	Rain Duration	Relative Humidity (Morning)	Relative Humidity (Afternoon)
918.06	74.82	271.10	2.08	295.40	2.86	0.00	0.00	42.42	36.16
917.35	71.40	101.94	2.44	140.47	3.53	0.00	0.00	24.33	19.43
923.04	60.64	51.00	17.07	63.70	22.10	0.00	20.00	8.90	14.46
920.50	70.14	198.83	4.34	211.20	5.19	0.00	0.00	12.19	12.74
921.16	44.29	277.80	1.86	136.50	2.86	8.90	14730.00	92.41	76.74

Preprocessing

Imputing Missing Values

Note that

def Data_info(Inp, Only_NaN = False):
    Out = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = Out.join(Inp.isnull().sum().to_frame(name = 'Number of NaN Values'), how='outer')
    Out['Percentage'] = np.round(100*(Out['Number of NaN Values']/Inp.shape[0]),2)
    if Only_NaN:
        Out = Out.loc[Out['Number of NaN Values']>0]
    return Out
    
Temp = Data_info(Data, Only_NaN = True)
display(Temp)
Temp = Temp.index.tolist()

	Data Type	Number of NaN Values	Percentage
Air Pressure	float64	3	0.27
Air Temperature	float64	5	0.46
Avg Wind Direction	float64	4	0.37
Avg Wind Speed	float64	3	0.27
Max Wind Direction	float64	3	0.27
Max Wind Speed	float64	4	0.37
Rain Accumulation	float64	6	0.55
Rain Duration	float64	3	0.27

imp = SimpleImputer(missing_values=np.nan, strategy='mean')
Data[Temp] = imp.fit_transform(Data[Temp])
Data_info(Data)

	Data Type	Number of NaN Values	Percentage
Air Pressure	float64	0	0.0
Air Temperature	float64	0	0.0
Avg Wind Direction	float64	0	0.0
Avg Wind Speed	float64	0	0.0
Max Wind Direction	float64	0	0.0
Max Wind Speed	float64	0	0.0
Rain Accumulation	float64	0	0.0
Rain Duration	float64	0	0.0
Relative Humidity (Morning)	float64	0	0.0
Relative Humidity (Afternoon)	float64	0	0.0

Problem Description

Let's set Relative Humidity (Afternoon) as the target variable. This means given the dataset and using the rest of the features, we would like to know whether is humid or not at 3 PM. In doing so, we can consider the median of Relative Humidity (Afternoon). Then, assign 1 to values over or equal the median value, and 0 to values under the median value.

Median = Data['Relative Humidity (Afternoon)'].median()
Temp = Data['Relative Humidity (Afternoon)']> Median
Temp = Temp.astype(int)
Target = 'Relative Humidity (Afternoon)'

Modeling

First off, let's look at the variance of our dataset features.

display(Data.iloc[:,:-1].var().sort_values(ascending = False).to_frame(name= 'Variance')\
        .style.background_gradient(cmap='OrRd').set_precision(2))

	Variance
Rain Duration	2546852.52
Avg Wind Direction	4762.57
Max Wind Direction	4508.55
Relative Humidity (Morning)	648.83
Air Temperature	124.32
Max Wind Speed	31.23
Avg Wind Speed	20.67
Air Pressure	10.11
Rain Accumulation	2.53

Furthermore, we would like to standardize features by removing the mean and scaling to unit variance. In this article, we demonstrated the benefits of scaling data using StandardScaler().

df = Data.copy()
df['Relative Humidity (Afternoon)'] = Temp
scaler = StandardScaler()
df.iloc[:,:-1] = scaler.fit_transform(df.iloc[:,:-1])

display(df.iloc[:,:-1].var().sort_values(ascending = False).to_frame(name= 'Variance')\
       .style.background_gradient(cmap=sns.light_palette("green", as_cmap=True)).set_precision(2))

	Variance
Rain Duration	1.00
Air Temperature	1.00
Max Wind Direction	1.00
Avg Wind Direction	1.00
Max Wind Speed	1.00
Avg Wind Speed	1.00
Relative Humidity (Morning)	1.00
Air Pressure	1.00
Rain Accumulation	1.00

df.describe().style.set_precision(2)

	Air Pressure	Air Temperature	Avg Wind Direction	Avg Wind Speed	Max Wind Direction	Max Wind Speed	Rain Accumulation	Rain Duration	Relative Humidity (Morning)	Relative Humidity (Afternoon)
count	1095.00	1095.00	1095.00	1095.00	1095.00	1095.00	1095.00	1095.00	1095.00	1095.00
mean	0.00	0.00	-0.00	-0.00	0.00	-0.00	0.00	-0.00	0.00	0.50
std	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	0.50
min	-3.43	-2.53	-1.84	-1.06	-1.79	-1.04	-0.13	-0.18	-1.11	0.00
25%	-0.73	-0.68	-1.10	-0.72	-1.08	-0.71	-0.13	-0.18	-0.75	0.00
50%	0.01	0.07	0.34	-0.36	0.42	-0.37	-0.13	-0.18	-0.43	0.00
75%	0.72	0.76	0.71	0.40	0.78	0.33	-0.13	-0.18	0.44	1.00
max	3.28	3.05	2.92	3.97	2.43	4.09	14.99	10.91	2.29	1.00

X = df.drop(columns = [Target])
y = df[Target]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
pd.DataFrame(data={'Set':['X_train','X_test','y_train','y_test'],
               'Shape':[X_train.shape, X_test.shape, y_train.shape, y_test.shape]}).set_index('Set').T

Set	X_train	X_test	y_train	y_test
Shape	(766, 9)	(329, 9)	(766,)	(329,)

A number of functions that we would use.

def Performance(clf, X_test):
    df = pd.DataFrame()
    y_pred = clf.predict(X_test)
    df = df.append({'Classifier': re.sub(r"(\w)([A-Z])", r"\1 \2",re.findall('[^()]+', str(clf))[0]),
                    'Score': clf.score(X_test, y_test),
                    'F1 Score': f1_score(y_test.values, y_pred, average= 'weighted'),
                    'Precision Score': precision_score(y_test.values, y_pred, average= 'weighted'),
                    'Recall Score':  recall_score(y_test.values, y_pred, average= 'weighted')}, ignore_index=True)
    display(df.style.hide_index().set_precision(2))
    return df

def highlight_max(s):
    is_max = s == s.max()
    return ['background-color: SpringGreen' if v else '' for v in is_max]


def Feature_Ranking(clf):
    df = pd.DataFrame()
    for n in range(2, X.shape[1]):
        selector = RFE(estimator= clf, n_features_to_select=n, verbose=0)
        selector.fit(X_train, y_train)
        df = df.append({'Number of Features to Select': n,
                        'Score': accuracy_score(y_test, selector.predict(X_test)),
                        'Features': X.columns[selector.support_].tolist(),
                        'Best Features':X.columns[selector.ranking_ == 1].tolist()}, ignore_index=True)

    df = df[['Number of Features to Select', 'Score', 'Features', 'Best Features']]
    df['Number of Features to Select'] = df['Number of Features to Select'].astype(int)
    df['Score'] = df['Score'].round(2)
    display(df.style.apply(highlight_max, subset=['Score']))
    return df.loc[df.Score == df.Score.max(), 'Features'].values[0]

def ROC_Curve(clf, X_test):
    # false positive rates, true positive rates and thresholds
    fpr, tpr, threshold = roc_curve(y_test, clf.predict_proba(X_test)[:,1])

    fig, ax = plt.subplots(1, 1, figsize=(5.5, 5.5))
    _ = ax.plot(fpr, tpr, lw=2, label = 'AUC = %0.2f' % auc(fpr, tpr))
    _ = ax.plot([0, 1], [0, 1],'r--', lw=2)
    _ = ax.legend(loc = 'lower right', fontsize = 14)
    delta =0.01
    _ = ax.set_xlim([-delta,1+delta])
    _ = ax.set_ylim([-delta,1+delta])
    _ = ax.set_xlabel('False Positive Rate (FPR)')
    _ = ax.set_ylabel('True Positive Rate (TPR)')
    return pd.DataFrame({'false positive rates':fpr, 'true positive rates':tpr, 'thresholds':threshold})

def Confusion_Matrix_Plot(clf, X_train, y_train, X_test, y_test, Labels):
    # Train set
    y_pred = clf.predict(X_train)
    Confusion_Matrix = confusion_matrix(y_train, y_pred)

    fig, ax = plt.subplots(1, 2, figsize=(14, 4))
    fig.suptitle('Train Set', fontsize = 18)
    _ = sns.heatmap(Confusion_Matrix, annot=True, annot_kws={"size": 14}, cmap="Blues", ax = ax[0],
                   linewidths = 0.2, cbar_kws={"shrink": 1})
    _ = ax[0].set_xlabel('Predicted labels')
    _ = ax[0].set_ylabel('True labels'); 
    _ = ax[0].set_title('Confusion Matrix');
    _ = ax[0].xaxis.set_ticklabels(Labels)
    _ = ax[0].yaxis.set_ticklabels(Labels)

    Confusion_Matrix = Confusion_Matrix.astype('float') / Confusion_Matrix.sum(axis=1)[:, np.newaxis]
    _ = sns.heatmap(Confusion_Matrix, annot=True, annot_kws={"size": 14}, cmap="Greens", ax = ax[1],
                   linewidths = 0.2, vmin=0, vmax=1, cbar_kws={"shrink": 1})
    _ = ax[1].set_xlabel('Predicted labels')
    _ = ax[1].set_ylabel('True labels'); 
    _ = ax[1].set_title('Normalized Confusion Matrix');
    _ = ax[1].xaxis.set_ticklabels(Labels)
    _ = ax[1].yaxis.set_ticklabels(Labels)

    for i in range(len(ax)):
        _ = ax[i].set_aspect(aspect=1.0)
        
    # Test set
    y_pred = clf.predict(X_test)
    Confusion_Matrix = confusion_matrix(y_test, y_pred)

    fig, ax = plt.subplots(1, 2, figsize=(14, 4))
    fig.suptitle('Test Set', fontsize = 18)
    _ = sns.heatmap(Confusion_Matrix, annot=True, annot_kws={"size": 14}, cmap="Blues", ax = ax[0],
                    linewidths = 0.2, cbar_kws={"shrink": 1})
    _ = ax[0].set_xlabel('Predicted labels')
    _ = ax[0].set_ylabel('True labels'); 
    _ = ax[0].set_title('Confusion Matrix');
    _ = ax[0].xaxis.set_ticklabels(Labels)
    _ = ax[0].yaxis.set_ticklabels(Labels)

    Confusion_Matrix = Confusion_Matrix.astype('float') / Confusion_Matrix.sum(axis=1)[:, np.newaxis]
    _ = sns.heatmap(Confusion_Matrix, annot=True, annot_kws={"size": 14}, cmap="Greens", ax = ax[1],
                   linewidths = 0.2, vmin=0, vmax=1, cbar_kws={"shrink": 1})
    _ = ax[1].set_xlabel('Predicted labels')
    _ = ax[1].set_ylabel('True labels'); 
    _ = ax[1].set_title('Normalized Confusion Matrix');
    _ = ax[1].xaxis.set_ticklabels(Labels)
    _ = ax[1].yaxis.set_ticklabels(Labels)
    
    for i in range(len(ax)):
        _ = ax[i].set_aspect(aspect=1.0)

DecisionTreeClassifier

First, let's try scikit-learn Decision Tree Classifier.

dtc = DecisionTreeClassifier()
_ = dtc.fit(X_train, y_train)
Performance_dtc = Performance(dtc, X_test)
ROC_dtc = ROC_Curve(dtc, X_test)

Classifier	F1 Score	Precision Score	Recall Score	Score
Decision Tree Classifier	0.87	0.87	0.87	0.87

Best_Features_dtc = Feature_Ranking(dtc)
print(Back.BLACK + Fore.CYAN + Style.NORMAL + 'Best Features:')
print(Style.RESET_ALL)
print('%s' % ', '.join(Best_Features_dtc))

	Number of Features to Select	Score	Features	Best Features
0	2	0.850000	['Max Wind Direction', 'Relative Humidity (Morning)']	['Max Wind Direction', 'Relative Humidity (Morning)']
1	3	0.880000	['Air Pressure', 'Air Temperature', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Relative Humidity (Morning)']
2	4	0.860000	['Air Pressure', 'Air Temperature', 'Max Wind Direction', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Max Wind Direction', 'Relative Humidity (Morning)']
3	5	0.870000	['Air Pressure', 'Air Temperature', 'Max Wind Direction', 'Max Wind Speed', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Max Wind Direction', 'Max Wind Speed', 'Relative Humidity (Morning)']
4	6	0.870000	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Max Wind Direction', 'Max Wind Speed', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Max Wind Direction', 'Max Wind Speed', 'Relative Humidity (Morning)']
5	7	0.890000	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Avg Wind Speed', 'Max Wind Direction', 'Max Wind Speed', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Avg Wind Speed', 'Max Wind Direction', 'Max Wind Speed', 'Relative Humidity (Morning)']
6	8	0.890000	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Avg Wind Speed', 'Max Wind Direction', 'Max Wind Speed', 'Rain Accumulation', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Avg Wind Speed', 'Max Wind Direction', 'Max Wind Speed', 'Rain Accumulation', 'Relative Humidity (Morning)']

Best Features:

Air Pressure, Air Temperature, Avg Wind Direction, Avg Wind Speed, Max Wind Direction, Max Wind Speed, Relative Humidity (Morning)

dtc = DecisionTreeClassifier()
_ = dtc.fit(X_train[Best_Features_dtc], y_train)
Performance_dtc = Performance(dtc, X_test[Best_Features_dtc])
ROC_dtc = ROC_Curve(dtc, X_test[Best_Features_dtc])

Confusion_Matrix_Plot(dtc, X_train[Best_Features_dtc], y_train,
                      X_test[Best_Features_dtc], y_test, Labels = ['Not Humid','Humid'])

Classifier	F1 Score	Precision Score	Recall Score	Score
Decision Tree Classifier	0.89	0.89	0.89	0.89

Random Forest Classifier

Next, let's use scikit-learn Random Forest Classifier.

rfc = RandomForestClassifier()
_ = rfc.fit(X_train, y_train)
Performance_rfc = Performance(rfc, X_test)
ROC_rfc = ROC_Curve(rfc, X_test)

Classifier	F1 Score	Precision Score	Recall Score	Score
Random Forest Classifier	0.92	0.92	0.92	0.92

Best_Features = Feature_Ranking(rfc)
print(Back.BLACK + Fore.CYAN + Style.NORMAL + 'Best Features:')
print(Style.RESET_ALL)
print('%s' % ', '.join(Best_Features))

	Number of Features to Select	Score	Features	Best Features
0	2	0.870000	['Air Pressure', 'Relative Humidity (Morning)']	['Air Pressure', 'Relative Humidity (Morning)']
1	3	0.910000	['Air Pressure', 'Max Wind Direction', 'Relative Humidity (Morning)']	['Air Pressure', 'Max Wind Direction', 'Relative Humidity (Morning)']
2	4	0.900000	['Air Pressure', 'Air Temperature', 'Max Wind Direction', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Max Wind Direction', 'Relative Humidity (Morning)']
3	5	0.910000	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Max Wind Direction', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Max Wind Direction', 'Relative Humidity (Morning)']
4	6	0.910000	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Max Wind Direction', 'Max Wind Speed', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Max Wind Direction', 'Max Wind Speed', 'Relative Humidity (Morning)']
5	7	0.920000	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Avg Wind Speed', 'Max Wind Direction', 'Max Wind Speed', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Avg Wind Speed', 'Max Wind Direction', 'Max Wind Speed', 'Relative Humidity (Morning)']
6	8	0.930000	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Avg Wind Speed', 'Max Wind Direction', 'Max Wind Speed', 'Rain Duration', 'Relative Humidity (Morning)']	['Air Pressure', 'Air Temperature', 'Avg Wind Direction', 'Avg Wind Speed', 'Max Wind Direction', 'Max Wind Speed', 'Rain Duration', 'Relative Humidity (Morning)']

Best Features:

Air Pressure, Air Temperature, Avg Wind Direction, Avg Wind Speed, Max Wind Direction, Max Wind Speed, Rain Duration, Relative Humidity (Morning)

rfc = RandomForestClassifier()
_ = rfc.fit(X_train[Best_Features], y_train)
Performance_rfc = Performance(rfc, X_test[Best_Features])
ROC_rfc = ROC_Curve(rfc, X_test[Best_Features])


Confusion_Matrix_Plot(dtc, X_train[Best_Features_dtc], y_train,
                      X_test[Best_Features_dtc], y_test, Labels = ['Not Humid','Humid'])

Classifier	F1 Score	Precision Score	Recall Score	Score
Random Forest Classifier	0.92	0.92	0.92	0.92

Final Thoughts

pd.concat([Performance_dtc, Performance_rfc], ignore_index=True).style.hide_index().\
                                                                    background_gradient(cmap='Greens').set_precision(2)

Classifier	F1 Score	Precision Score	Recall Score	Score
Decision Tree Classifier	0.89	0.89	0.89	0.89
Random Forest Classifier	0.92	0.92	0.92	0.92

fig = go.Figure()
fig.add_trace(go.Scatter(x= ROC_dtc['false positive rates'].values, y= ROC_dtc['true positive rates'].values,
                         line=dict(color='Blue', width= 1.5), 
                         name = '<b>Decision Tree</b>'))
fig.add_trace(go.Scatter(x= ROC_rfc['false positive rates'].values, y= ROC_rfc['true positive rates'].values,
                         line=dict(color='Green', width= 1.5), 
                         name = '<b>Random Forest</b>'))
fig.add_trace(go.Scatter(x= [0,1], y= [0,1], showlegend = False, line=dict(color='black', width=2, dash='dot')))
fig.update_layout(legend_title='Classifiers')
fig.update_layout(legend=dict(y=0.5, traceorder='reversed', font_size=12))
fig.update_layout(dragmode='select', plot_bgcolor= 'white',
                  width=610, height=500, hovermode='closest')
delta = 1e-2
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray', range=[-delta, 1+delta],
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                zeroline=True, zerolinewidth=1, zerolinecolor='Lightgray')
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray', range=[-delta, 1+delta],
                showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                zeroline=True, zerolinewidth=1, zerolinecolor='Lightgray')
fig.update_layout(title={'text': 'ROC Curves',
                         'x':0.43, 'y':0.85,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

We can see the area under the curve for Random Forest Classifier is better, therefore, this classifier performs the best here.

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